Modeling the Effect of the Spatial Pattern of Airborne Lidar Returns on the Prediction and the Uncertainty of Timber Merchantable Volume
Bibliographic record
Abstract
Lidar data are regularly used to characterize forest structures. In this study, we determine the effects of three lidar attributes (density, spacing, scanning angle) on the accuracy and the uncertainty of timber merchantable volume estimates of balsam fir stands (Abies balsamea (L.) Mill.) in eastern Canada. We used lidar point clouds to compute predictor variables of the merchantable volume in a nonlinear model. The best model included the mean height of first returns, the proportion of first returns below 2 m and the canopy surface roughness index. Our analysis shows a high correlation between lidar and field data of 119 plots (pseudo-R2 = 0.91), however, residuals were heteroscedastic. More precise parameter estimates were obtained by adding to the model a variance function of variables describing the mean height of returns and the skewness of the area distribution of triangulated lidar returns. The residual standard deviation was better estimated (3.7 m3 ha−1 multiplied by the variance function versus 28.0 m3 ha−1). We found no effect of density on the predictions (p-value = 0.74). This suggests that the height and the spatial pattern of returns, rather than the density, should be considered to better assess the uncertainty of merchantable volume estimates.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".